Implementasi Data Mining Dalam Mengelompokkan Jumlah Penduduk Miskin Berdasarkan Provinsi Menggunakan Algoritma K-Means - Yuni Radana Sembiring; S Saifullah; Riki Winanjaya (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2021)
Poverty is a certain condition that is below the standard line of minimum needs, good for food and non-food. Poor households generally have a greater average number of members compared to households that only have members who have fewer members. This situation is followed by the low level of education of household heads and workers who generally only work in the agricultural sector. Factors such as education, labor, health, fertility, housing, and the environment are a picture of the level of people’s welfare which is tought to affect the amount of proverty. This study used data sourced from the Central Bureau of statistics the year 2007-2019. The method used is Datamining the K-Means Clustering, Clustering is a method used in datamining the how it works find and classify data that has a semblance and characteristics of data between one another with the data. Using this algorithm the data already obtained can be grouped into Clusters based on this data. This data can be entered to the local Government to recommend to the Government so that the Government can handle the number of poor people in this country.